Predicting short-term mid-price movements from limit order book (LOB) data is a fundamental problem in quantitative finance and market microstructure research, with direct applicability to both traditional exchanges and cryptocurrency markets—including centralized exchanges (CEXs) and emerging on-chain LOB protocols in decentralized finance (DeFi). We present three contributions to this domain. First, we propose DA-BiGRU-CNN, a domain-aware dual-branch architecture that decomposes LOB features into price and volume information channels, processes them through dedicated bidirectional GRU encoders with shared microstructure features, and fuses temporal representations via a multi-scale convolutional bottleneck (Conv1d with kernels k = 3,5,7). Second, we provide empirical evidence for a "feature sufficiency" hypothesis: a unidirectional GRU trained on 53 basic features achieves performance statistically equivalent to one trained on 219 extensively engineered features—including rolling statistics, exponential moving averages, and lag/difference features—suggesting that recurrent hidden states implicitly learn these temporal patterns. Third, we document a "negative ensemble effect" where combining sequential (GRU) and tabular (gradient boosting) models consistently degrades prediction quality, contradicting the widely-held assumption that model diversity improves ensemble performance. On a large-scale dataset of 12,165 LOB sequences (12.1M timesteps), our GRU baseline achieves a weighted Pearson correlation of 0.266, outperforming LightGBM by 58%, while our domain-aware architecture offers an architecturally principled alternative that naturally separates price dynamics from liquidity dynamics.
This paper proposes Global Adaptive Equity Pricing (GAEP), a novel AI-driven framework for moderating economic inequality through real-time, consumption-event-based price personalization. At each domestic purchase, biometric verification links to encrypted networth data to compute a progressive adjusted price using the Wealth Elasticity Pricing Equation (WEPE). Excess payments from higher-net-worth individuals fund a transparent Gini Moderation Fund (GMF) for AI-optimized redistribution targeting a blended Gini coefficient of ≈0.30. Tunable parameters enable governments to control moderation velocity, balancing equity gains against capital retention risks in wealth-attracting jurisdictions. Calibrated to Singapore's 2025-2026 data (income Gini after transfers and taxes: 0.379; market income Gini before transfers: 0.452; wealth Gini: 0.55; top 1% hold ~14%, top 5% ~33% of household wealth), agent-based simulations project 15-41% Gini reductions over 20 quarterly cycles. Ethical safeguards include zero-knowledge proofs, blockchain-audited aggregates (no personal data exposure), fairness audits, and positive incentives. GAEP extends Gini theory and computational economics by integrating biometric technology with redistributive algorithms, distinct from usage-tiered tariffs or surveillance pricing. It offers policymakers a pathway for dynamic, consumption-led equity in AI-augmented economies while preserving innovation incentives.
Generative and agentic artificial intelligence is entering financial markets faster than existing governance can adapt. Current modelrisk frameworks assume static, well-specified algorithms and onetime validations; large language models and multi-agent trading systems violate those assumptions by learning continuously, exchanging latent signals, and exhibiting emergent behavior. Drawing on complex adaptive systems theory, we model these technologies as decentralized ensembles whose risks propagate along multiple timescales. We then propose a modular governance architecture. The framework decomposes oversight into four layers of "regulatory blocks": (i) self-regulation modules embedded beside each model, (ii) firm-level governance blocks that aggregate local telemetry and enforce policy, (iii) regulator-hosted agents that monitor sector-wide indicators for collusive or destabilizing patterns, and (iv) independent audit blocks that supply third-party assurance. Eight design strategies enable the blocks to evolve as fast as the models they police. A case study on emergent spoofing in multiagent trading shows how the layered controls quarantine harmful behavior in real time while preserving innovation. The architecture remains compatible with today's model-risk rules yet closes critical observability and control gaps, providing a practical path toward resilient, adaptive AI governance in financial systems.
We study the epistemic efficiency of decentralized prediction markets under autonomous agentic liquidity. We introduce the information-incentive gap (G) – the discrepancy between ground truth and the market-implied probability – and establish, via Itô's calculus and exact solution of the resulting moment ODE, exponential convergence of its second moment together with an explicit upper bound for the gap of order O(σ/λ−−√). A two-level empirical study on information-driven event categories (Politics, Economics, Finance, Crypto Markets), drawing on approximately 40 million time-series records collected over the study period, is consistent with the model: (i) platform-level analysis of N=100 resolved binary events per platform shows the mean gap decreasing from G¯=0.517 at T−168 h to G¯=0.229 at T−30 min for Kalshi, and from 0.583 to 0.002 for Polymarket, with an empirical convergence rate λemp≈1.4×10−6 s−1; (ii) a paired cross-platform comparison of N=34 matched event groups shows that Polymarket exhibits a lower mean gap than Kalshi (mean ΔG=0.27 at T−6 h; Polymarket leads in 85% of pairs), consistent with the theoretical dependence of convergence speed on liquidity-driven λ. Monte Carlo simulation (N=50000 paths) confirms a >276× reduction in convergence latency and a 109× improvement in the Information Efficiency Ratio (IER) compared to the human-centric baseline.
Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposesCryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.
João Pires da Cruz, Daniel Costa, Pedro Granate, Armando Teixeira · 6 authors
We study the formation and evolution of trading networks in non-fungible token (NFT) markets using transaction-level data from two major collections, Bored Ape Yacht Club (BAYC) and Azuki. We introduce a simple transaction-based clustering rule that identifies dynamically evolving trading networks formed by buyer-seller interactions. These networks correspond to persistent trading structures linking wallets through sequences of transactions. We document three main empirical regularities. First, trading networks emerge endogenously and exhibit heavy-tailed size distributions consistent with preferential attachment dynamics. Second, the internal connectivity of large networks displays scale-free degree distributions characteristic of growing trading systems. Third, the lifetime of trading networks follows approximately exponential statistics, indicating a memoryless extinction process. These findings suggest that NFT markets are organized around evolving clusters of trading relationships rather than isolated transactions. The results replicate across collections, indicating that trading network formation is a robust structural feature of NFT markets. Our findings provide new evidence on the microstructure of digital asset markets and the mechanisms governing the formation and persistence of trading relationships.
João Pires da Cruz, Daniel Costa, Armando Teixeira, João B. Duarte · 6 authors
We analyze the Ethereum transaction network using a spectral decomposition based on functional edge modes. Each transaction is represented as a complex amplitude indexed by the combined connectivity of the interacting addresses, and amplitudes are aggregated into mode-resolved coherent sums. Applying this construction to a snapshot of native ETH transfers from the second half of 2015 (∼1.9 × 10 6 transactions across 26,937 addresses), we identify spectral modes whose coherent power significantly exceeds that obtained under randomized phase baselines. Statistical significance is assessed via B = 1000 phase permutations with multiple-testing correction: 469 of 928 modes (50.5%) survive Benjamini-Hochberg control at the 5% false discovery rate, while none survive the more conservative Bonferroni threshold. Strong global coherence is primarily driven by high-degree nodes: removing the top 0.1% of nodes by degree (27 hubs) collapses the bulk of the spectrum towards the randomized baseline. However, statistically significant residual coherence persists across roughly half of the tested modes, indicating that organization in the network is not purely an artifact of hub aggregation. We frame these findings through a quantum-like analogy in which phase-aligned edge contributions interfere constructively, and discuss implications for the structural analysis of decentralized financial systems.
O presente artigo formaliza o <i>Economic Centrifugal Dispersion Model</i> (ECDM) como uma estrutura analítica de alta fidelidade para a compreensão da propagação de capital e incentivos em ecossistemas de Web3 e finanças descentralizadas (DeFi). Fundamentado em uma convergência interdisciplinar entre a praxeologia da escola austríaca, a física estatística e a dinâmica de sistemas complexos, o modelo propõe que a injeção monetária em sistemas baseados em blockchain gera forças dispersivas análogas às forças centrífugas. A pesquisa detalha a aplicação do operador de Lyapunov para avaliar a estabilidade e a resiliência desses fluxos sob condições de volatilidade estocástica.<br>
This paper introduces Autonomous Mechanism Economics (AME), a theoretical framework for analyzing economic systems where human discretion is removed from mechanism execution. While classical mechanism design theory (Hurwicz, 1960; Maskin, 1999; Myerson, 1981) focuses on designing incentive-compatible rules, it implicitly assumes human agents execute these rules. We formalize a new class of economic mechanisms-Autonomous Mechanisms (AM)-where execution is performed by deterministic, immutable code rather than discretionary human agents. We establish four core theoretical results. First, Non-Discretionary Buyback (NDB) mechanisms minimize execution-layer agency costs (Theorem 1). Second, assets satisfying specific structural conditions-revenue increasing in market volatility combined with NDB execution-may exhibit antifragility, generating positive expected returns during market stress (Theorem 2). Third, when algorithmic buying capacity exceeds maximum individual selling capacity, markets may undergo threshold transitions to qualitatively different dynamics (Theorem 3). Fourth, USDdenominated staking requirements create self-reinforcing supply dynamics with bounded equilibrium returns (Theorem 4). We connect this framework to Kydland and Prescott (1977)’s “rules versus discretion” literature, arguing that AM protocols may represent a strong rules-based solution by eliminating not merely the incentive but potentially the ability to deviate from prescribed rules. Using data from Hyperliquid—a decentralized exchange implementing NDB at scale—we provide preliminary empirical support, documenting a volume-volatility correlation of 0.627 (p
We consider the interaction between centralized trading and decentralized Proof of Stake (PoS) blockchain ecosystems. Motivated by the increasing dominance of centralized exchanges and the institutionalization of crypto markets, we study how trading activities on centralized exchanges affect staking behavior, token allocation, and decentralization within a PoS blockchain. We formulate a continuous-time mean field model, where the miners simultaneously act as validators in the PoS protocol and traders in a centralized market with price impact. Under suitable assumptions, we establish the local well-posedness of the mean field system, and derive a semi-explicit characterization of the equilibrium trading strategy. Numerical results suggest that centralized trading activities may enhance staking participation, and promote decentralization of the staking distribution through market incentives. We also study the effects of transaction costs and token supply mechanisms on the equilibrium staking ratio and concentration profile. These results illustrate how market microstructure and centralized liquidity provision can exert significant influence on decentralized blockchain protocols.
Este artigo representa uma expansão analítica e quantitativa do Economic Centrifugal Dispersion Model (ECDM), consolidando-o como um framework de "Termodinâmica Criptoeconômica". Enquanto o estudo anterior estabeleceu as bases espaciais e monetárias da força centrífuga econômica, esta continuação aprofunda a modelagem através de equações diferenciais não lineares e introduz o DAO Chaos Index (DCI) para mensurar a instabilidade em governanças descentralizadas.
Este artigo explora a transposição analógica e matemática da Equação de Rayleigh, originalmente concebida no domínio da física acústica e teoria de sinais, para a modelagem de fenômenos complexos em ambientes Web3 e infraestruturas de blockchain. A pesquisa fundamenta-se na premissa de que a distribuição de Rayleigh, ao descrever a magnitude de vetores compostos por componentes gaussianas independentes, oferece um arcabouço robusto para analisar a volatilidade de criptoativos, a latência de propagação de rede, e a resiliência de sistemas de consenso. Através de uma abordagem interdisciplinar que integra econofísica, teoria de sinais e auditoria algorítmica, o estudo discute como a variabilidade estocástica impacta a segurança e a eficiência de protocolos descentralizados. São analisadas as conexões entre a distribuição de Rayleigh e a Lei de Benford na detecção de fraudes em tokenomics, além de contrastar o Efeito Cantillon com o Efeito Nakamoto na distribuição de riqueza digital. Por fim, propõe-se o Modelo de Resiliência Estocástica ECDM (Environment, Consensus, Distribution, Magnitude) como uma ferramenta preditiva para a governança e auditoria de ecossistemas blockchain.<br>
O presente diagnóstico econômico investiga as falhas estruturais e as vulnerabilidades sistêmicas inerentes aos modelos de engenharia econômica da Web3, fundamentando-se no princípio da não neutralidade da moeda conhecido como Efeito Cantillon. A pesquisa articula como a distribuição assimétrica inicial de tokens, frequentemente favorecendo fundadores e investidores institucionais, estabelece uma assinatura econômica de fragilidade que compromete a descentralização e a sustentabilidade dos protocolos digitais. Através de uma abordagem interdisciplinar, o relatório integra o conceito de Doppler Econômico para explicar a defasagem informacional entre agentes privilegiados e o público geral, além de utilizar a metáfora do Mammoth Money para descrever dinâmicas de predação de capital. Para a verificação empírica, aplica-se a Lei de Benford como ferramenta de auditoria estatística e a Curva de Laffer para determinar os limites de incentivos de emissão. O estudo conclui que a resiliência dos ecossistemas Web3 depende de um redesenho fundamental dos mecanismos de alocação inicial e de uma transparência radical que mitigue as distorções perceptivas e econômicas que levam a colapsos catastróficos e eventos de Cisne Negro.<br>
<p>Resumo <br>Este artigo investiga a viabilidade econômica e comportamental da criação de criptomoedas <br>personalizadas (Fan Coins) atreladas à performance de jogadores de futebol, utilizando métodos <br>quantitativos em Econometria, com foco em arrecadação por bilheteria, patrocínios e apostas esportivas. A <br>análise inclui os casos de Diego Ribas (Flamengo), Neymar (Santos), e artilheiros do São Paulo e Palmeiras. <br>A proposta é analisar como a reputação e a performance esportiva podem ser transformadas em ativos <br>digitais de valor mensurável. Ainda, propõe-se a utilização de sistemas de inteligência artificial para gestão <br>de carteiras de patrocinadores e um aplicativo de fan clube com sistema de assinaturas para fomentar um <br>novo modelo de negócios esportivos baseado em dados e personalização. <br>Palavras-chave: Fan Coin; Criptomoeda; Econometria; Economia do Esporte; Teoria dos <br>Jogos; Finanças Comportamentais; Apostas Esportivas; Inteligência Artificial; Modelagem <br>Financeira; Patrocínio Digital.</p>
Autonomous financial agents, powered by the convergence of artificial intelligence and blockchain technology, represent a paradigm shift in decentralized finance. These self-operating entities now possess capabilities to hold cryptocurrency wallets, execute complex transactions, and even launch tokens without human oversight. The architectural framework supporting these agents integrates specialized language models, secure wallet management systems, and persistent on-chain identities. From market-making to yield optimization, these agents demonstrate remarkable efficacy across various financial operations, creating novel market dynamics when interacting with both human participants and other autonomous systems. Essential to mainstream adoption are sophisticated reputation frameworks combining algorithmic assessment with social consensus mechanisms. However, significant challenges exist, including market manipulation vulnerabilities, spam production, and regulatory complexity. As these autonomous agents continue evolving, appropriate governance models tailored to agent characteristics become critical for balancing innovation with market integrity in this emerging financial landscape.
Consider a social-choice function (SCF) is chosen to decide votes in a formal system, including votes to replace the voting method itself. Agents vote according to their ex-ante belief over what decisions are considered, and whether they prefer them to be decided by the incumbent SCF or the suggested replacement. The existing SCF then aggregates the agents' votes and arrives at a decision of whether it should itself be replaced. An SCF is self-maintaining if it can not be replaced in such fashion by any other SCF. Our focus is on the implications of self-maintenance for centralization. For this purpose, unlike [Barbera and Jackson, 2004], we do not generally restrict attention to anonymous SCFs. We also do not restrict attention to neutral SCFs, unlike [Koray, 2000]. We present results considering optimistic, pessimistic and i.i.d. approaches with respect to agent beliefs, different tie-breaking rules, and different SCF domains. To highlight two of the results, (i) for the i.i.d. unbiased case with arbitrary tie-breaking and general Boolean functions, we prove an Arrow-Style Theorem for Dynamics: We show that only a dictatorship is self-maintaining, and any other SCF has a path of changes that arrives at a dictatorship. (ii) With a pessimistic approach, tie-breaking that prefers the status quo, and WMGs, we provide a tight characterization of the self-maintaining rules, which are exactly all games with minimal winning coalitions of size at most 2. We then consider two extensions, (i) forward-looking voters, (ii) Where the voter utility depends on wisdom of the crowd effects. In both cases, less centralized SCFs become self-maintaining. All in all we provide a basic framework and body of results for centralization dynamics and stability, applicable for institution design, especially in formal De-Jure systems, such as Blockchain Decentralized Autonomous Organizations (DAOs).
Cryptocurrencies market capitalization has surpassed $4 trillion in 2025, attracting individual and institutional traders seeking investment and speculation. However, volatility of cryptocurrencies prices makes profitable strategies a huge challenge, especially with respect to the variance of returns. In this context, this paper presents an innovative strategy based on the counterintuitive concept from Game Theory called Parrondo’s Paradox. The presented strategy results in improved capital gains (returns) when compared to traditional buy & hold. Also, the strategy is proven to work in daily, weekly and minute-by-minute timeframes. With the empirical results shown in this paper, the Parrondo’s Paradox framework can be used as a trading strategy by either individual or institutional investors.
Historical Context and Problem Statement The digital revolution has created two parallel challenges that have resisted comprehensive solutions: Internet Data Transfer Limitations: Despite decades of progress, internet download speeds remain constrained by inefficient protocols that don't adapt to network topology dynamics. Traditional download managers like IDM operate with static segmentation strategies that ignore the quantum-inspired probabilistic nature of network paths. Web3 Liquidity Fragmentation: Decentralized finance (DeFi) suffers from fragmented liquidity across multiple venues, resulting in significant MEV exploitation. As documented by Qin et al. (2021), MEV extraction has cost users over $680 million in 2021 alone, with no comprehensive solution addressing the root cause. These seemingly disconnected problems share a common underlying structure: both involve the transfer of "value" (data or financial assets) across complex networks where efficiency is hampered by non-resonant transmission strategies.
Stanisław Drożdż, Paweł Jarosz, Jarosław Kwapień, Maria Skupień · 5 authors
Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctuation-dependent correlation matrices using the multifractal detrended cross-correlation coefficient ρr that selectively emphasizes fluctuations of different amplitudes. We examine the spectral properties of these detrended correlation matrices and compare them to the spectral properties of the matrices calculated in the same way from synthetic Gaussian and q-Gaussian signals. Our results show that detrending, heavy tails, and the fluctuation-order parameter r jointly produce spectra, which substantially depart from the random case even under the absence of cross-correlations in time series. Applying this framework to one-minute returns of 140 major cryptocurrencies from 2021 to 2024 reveals robust collective modes, including a dominant market factor and several sectoral components whose strength depends on the analyzed scale and fluctuation order. After filtering out the market mode, the empirical eigenvalue bulk aligns closely with the limit of random detrended cross-correlations, enabling clear identification of structurally significant outliers. Overall, the study provides a refined spectral baseline for detrended cross-correlations and offers a promising tool for distinguishing genuine interdependencies from noise in complex, nonstationary, heavy-tailed systems.